Commit 322ad298 authored by matbuoro's avatar matbuoro
Browse files

update 2016

parent b50c79ee
......@@ -32,22 +32,31 @@ source(paste('parameters_',stade,'.R',sep="")) # chargement des paramètres
#------------------------INITS----------------------------------##
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
#if(!file.exists(paste('inits/inits_',stade,year,'.Rdata',sep=""))){
if(!file.exists(paste("inits/init-",site,"-",stade,year,".txt",sep=""))){
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
#load(paste('inits/inits_',stade,year,'.Rdata',sep=""))
}
#load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
#if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))
#------------------------MODEL----------------------------------##
model <- paste("model/",stade,"-",site,".txt",sep="") # path of the model
if(site == "Scorff" && stade == "smolt") {model <- paste("model/",stade,"-",site,"_",year,".R",sep="")} # le modèle Scorrf pour les smolt peut changer tous les ans suivant conditions
model <- paste("model/model_",stade,"-",site,".R",sep="") # path of the model
if(site == "Scorff" && stade == "smolt") {model <- paste("model/model_",stade,"-",site,"_",year,".R",sep="")} # le modèle Scorrf pour les smolt peut changer tous les ans suivant conditions
model
filename <- file.path(work.dir, model)
#system(paste("cp",model,paste(stade,"-",site,".txt",sep=""),sep=""))
#---------------------------ANALYSIS-----------------------------##
nChains = length(inits) # Number of chains to run.
adaptSteps = 1000 # Number of steps to "tune" the samplers.
nburnin=1000 # Number of steps to "burn-in" the samplers.
nstore=5000 # Total number of steps in chains to save.
nthin=1 # Number of steps to "thin" (1=keep every step).
nburnin=5000 # Number of steps to "burn-in" the samplers.
nstore=25000 # Total number of steps in chains to save.
nthin=2 # Number of steps to "thin" (1=keep every step).
#nPerChain = ceiling( ( numSavedSteps * thinSteps ) / nChains ) # Steps per chain.
### Start of the run ###
......@@ -57,15 +66,25 @@ start.time = Sys.time(); cat("Start of the run\n");
fit <- bugs(
data
,inits
,model.file = model
,model.file = filename
,parameters
,n.chains = nChains, n.iter = nstore + nburnin, n.burnin = nburnin, n.thin = nthin
,DIC=FALSE
,codaPkg = FALSE, clearWD=TRUE
,codaPkg = FALSE, clearWD=FALSE
#,debug=TRUE
,working.directory=work.dir
,working.directory=paste(work.dir,"bugs",sep="/")
)
## cleaning
system("rm bugs/CODA*")
### Save inits ###
# save last values for inits
# inits <- fit$last.values
# if(site == "Nivelle") {
# save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
# }
######### JAGS ##########
## Compile & adapt
......@@ -94,7 +113,7 @@ cat("Sample analyzed after ", elapsed.time, ' minutes\n')
## BACKUP
save(fit,file=paste('results/results_',stade,"_",year,'.RData',sep=""))
save(fit,file=paste('results/Results_',stade,"_",year,'.RData',sep=""))
write.table(fit$summary,file=paste('results/Results_',stade,"_",year,'.csv',sep=""),sep=";")
#------------------------------------------------------------------------------
......@@ -159,6 +178,6 @@ dev.off()
#------------------------------------------------------------------------------
## SUMMARY
if(site == "Scorff" && stade == "adult") {source("summary_adult.R")}
#if(site == "Scorff" && stade == "adult") {source("summary_adult.R")}
if(site == "Nivelle" && stade == "tacon") {source("analyse_coda_tacon.R")}
list(Y=3.30000E+01, Q2pic=c(6.46629E+03, 5.32631E+03, 5.40180E+03, 8.41090E+03, 5.94366E+03, 4.92998E+03, 4.39932E+03, 4.50977E+03, 6.26053E+03, 6.03720E+03, 6.16809E+03, 5.03814E+03, 4.07344E+03, 4.33988E+03, 6.52310E+03, 5.20628E+03, 9.59656E+03, 8.61590E+03, 7.45590E+03, 5.32475E+03, 4.62180E+03, 4.77049E+03, 4.76180E+03, 5.71459E+03, 7.37787E+03, 5.54459E+03, 5.96213E+03, 4.41492E+03, 7.02574E+03, 8.21000E+03, 6.95230E+03, 6.00197E+03, 6.42836E+03), Cm_B= structure(.Data= c(6.00000E+00, 0.00000E+00, 2.00000E+01, 1.00000E+00, 9.00000E+00, 1.00000E+00, 6.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, NA, NA, 2.00000E+00, 0.00000E+00, 7.00000E+00, 0.00000E+00, 5.00000E+00, 2.00000E+00, NA, NA, 1.00000E+00, 0.00000E+00, 2.00000E+00, 1.00000E+00, 3.00000E+00, 3.00000E+00, 1.00000E+00, 0.00000E+00, 8.00000E+00, 2.00000E+00, 1.00000E+00, 0.00000E+00, NA, NA, NA, NA, 1.30000E+01, 5.00000E+00, 0.00000E+00, 1.00000E+00, 1.10000E+01, 2.00000E+00, 3.70000E+01, 2.00000E+00, 1.30000E+01, 3.00000E+00, 1.00000E+00, 0.00000E+00, 5.00000E+00, 0.00000E+00, 1.20000E+01, 4.00000E+00, 1.00000E+01, 2.00000E+00, 1.50000E+01, 1.00000E+00, 7.00000E+00, 1.00000E+00, 5.00000E+00, 0.00000E+00, 3.00000E+00, 2.00000E+00, 1.20000E+01, 2.00000E+00, 7.00000E+00, 1.00000E+00), .Dim=c(33, 2)), Cum_B= structure(.Data= c(2.00000E+00, 1.00000E+00, 4.00000E+00, 0.00000E+00, 5.00000E+00, 1.00000E+00, 3.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, NA, NA, 1.00000E+00, 0.00000E+00, 3.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, NA, NA, 1.00000E+00, 1.00000E+00, 1.50000E+01, 0.00000E+00, 3.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 4.00000E+00, 1.00000E+00, 1.00000E+00, 5.00000E+00, NA, NA, NA, NA, 1.20000E+01, 4.00000E+00, 0.00000E+00, 0.00000E+00, 7.00000E+00, 1.00000E+00, 2.40000E+01, 3.00000E+00, 6.00000E+00, 6.00000E+00, 5.00000E+00, 0.00000E+00, 4.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 3.00000E+00, 3.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 3.00000E+00, 3.00000E+00, 3.00000E+00, 3.00000E+00, 0.00000E+00, 2.00000E+00, 4.00000E+00, 0.00000E+00), .Dim=c(33, 2)), C_Eu= structure(.Data= c(4.30000E+01, 2.60000E+01, 7.80000E+01, 1.90000E+01, 1.19000E+02, 1.90000E+01, 9.80000E+01, 2.50000E+01, 5.30000E+01, 1.20000E+01, 1.21000E+02, 2.00000E+01, 5.00000E+01, 3.30000E+01, 1.18000E+02, 1.90000E+01, 1.23000E+02, 3.50000E+01, 3.30000E+01, 1.60000E+01, 2.60000E+01, 5.00000E+00, 2.30000E+01, 1.00000E+00, 1.80000E+01, 1.60000E+01, 2.40000E+01, 1.00000E+01, 1.77000E+02, 1.00000E+01, 1.20000E+01, 1.30000E+01, 7.00000E+00, 4.00000E+00, 8.00000E+00, 1.00000E+00, 5.60000E+01, 1.30000E+01, 1.30000E+01, 1.00000E+01, 4.50000E+01, 9.00000E+00, 1.59000E+02, 3.00000E+01, 7.30000E+01, 6.60000E+01, 3.10000E+01, 1.30000E+01, 7.80000E+01, 3.00000E+00, 7.10000E+01, 3.40000E+01, 1.05000E+02, 2.40000E+01, 1.23000E+02, 2.40000E+01, 9.70000E+01, 2.40000E+01, 9.30000E+01, 4.20000E+01, 6.40000E+01, 2.80000E+01, 1.06000E+02, 3.30000E+01, 1.16000E+02, 1.50000E+01), .Dim=c(33, 2)), Cm_Eu= structure(.Data= c(4.30000E+01, 2.50000E+01, 7.80000E+01, 1.90000E+01, 1.19000E+02, 1.90000E+01, 9.70000E+01, 2.50000E+01, 5.30000E+01, 1.20000E+01, 1.21000E+02, 2.00000E+01, 5.00000E+01, 3.30000E+01, 1.18000E+02, 1.80000E+01, 1.22000E+02, 3.50000E+01, 3.30000E+01, 1.60000E+01, 2.60000E+01, 5.00000E+00, 2.30000E+01, 1.00000E+00, 1.80000E+01, 1.60000E+01, 2.40000E+01, 1.00000E+01, 1.77000E+02, 1.00000E+01, 1.20000E+01, 1.30000E+01, 7.00000E+00, 4.00000E+00, 8.00000E+00, 1.00000E+00, 5.60000E+01, 1.30000E+01, 1.30000E+01, 1.00000E+01, 4.50000E+01, 9.00000E+00, 1.58000E+02, 3.00000E+01, 7.30000E+01, 6.60000E+01, 3.10000E+01, 1.30000E+01, 7.80000E+01, 3.00000E+00, 6.90000E+01, 3.40000E+01, 1.05000E+02, 2.40000E+01, 1.23000E+02, 2.40000E+01, 9.70000E+01, 2.40000E+01, 9.30000E+01, 4.20000E+01, 6.40000E+01, 2.80000E+01, 1.06000E+02, 3.30000E+01, 1.16000E+02, 1.50000E+01), .Dim=c(33, 2)), Q= structure(.Data= c(5.85829E+03, 6.92227E+03, 6.44961E+03, 7.80948E+03, 5.68619E+03, 7.14198E+03, 7.05350E+03, 8.18086E+03, 7.27622E+03, 9.88653E+03, 6.09725E+03, 7.68706E+03, 4.43132E+03, 5.44497E+03, 4.83354E+03, 5.48296E+03, 4.83453E+03, 5.18258E+03, 5.33822E+03, 6.11554E+03, 7.65069E+03, 1.03442E+04, 6.54582E+03, 9.48965E+03, 3.91842E+03, 4.36074E+03, 4.11933E+03, 4.63326E+03, 4.83993E+03, 5.27682E+03, 6.07481E+03, 8.03413E+03, 7.92128E+03, 9.98831E+03, 9.63821E+03, 1.35438E+04, 7.82115E+03, 1.01506E+04, 6.27615E+03, 7.72571E+03, 5.35449E+03, 5.40169E+03, 4.99821E+03, 5.23195E+03, 5.11295E+03, 5.46000E+03, 6.13000E+03, 6.14455E+03, 7.71436E+03, 9.16260E+03, 5.77282E+03, 6.83221E+03, 5.72487E+03, 7.06597E+03, 4.70705E+03, 5.03935E+03, 6.50833E+03, 7.24117E+03, 7.00500E+03, 8.45143E+03, 7.90731E+03, 8.91286E+03, 6.35885E+03, 7.58753E+03, 7.75462E+03, 8.47688E+03), .Dim=c(33, 2)))
list(lambda_tot0=1.04200E+02, logit_flow_Eu=c(-4.26200E-01, -7.07900E-01), lflow_fall_Eu=c(-5.00000E-01, -5.00000E-01), logit_int_Eu=c(5.08200E-01, 8.30400E-01), mupi_B=c(8.27000E-02, 1.03200E-01), pi_Eu00=c(5.87100E-01, 5.99200E-01), pi_Eu01=c(2.00600E-01, 7.99400E-01), rate_lambda=4.23800E-02, s=c(1.56700E+01, 3.91400E+00), shape_lambda=5.99500E+00, sigmapi_B=c(6.44000E-01, 6.96900E-01), sigmapi_Eu=c(1.01900E+00, 8.86500E-01), lambda_tot=c(1.02000E+02, 1.15500E+02, 2.20800E+02, 1.76200E+02, 1.30000E+02, NA, 1.24500E+02, 1.94300E+02, 1.57000E+02, NA, 9.30000E+01, 1.44000E+02, 6.23300E+01, 3.40000E+01, 2.80500E+02, 1.75000E+02, NA, NA, 1.30300E+02, 2.30000E+01, 8.72300E+01, 3.18200E+02, 2.43200E+02, 2.64000E+02, 1.62000E+02, 1.09400E+02, 1.93500E+02, 1.47000E+02, 1.51200E+02, 2.97000E+02, 2.02400E+02, 1.58900E+02, 1.96500E+02), logit_pi_Eu= structure(.Data= c(1.68200E+00, 3.92200E-02, NA, -7.12400E-01, NA, -1.57100E+00, NA, -9.26000E-01, 1.48500E+00, -1.48500E+00, NA, NA, 1.40600E+00, 1.20600E-01, NA, -1.41400E+00, NA, -2.17400E-01, NA, NA, 2.37700E-01, -2.11600E+00, -7.56300E-01, -4.26300E+00, 3.12700E-01, 5.34900E-02, NA, 3.56700E-01, NA, -2.56700E+00, -1.83900E+00, -1.74600E+00, NA, NA, NA, NA, 1.81000E+00, -1.38900E+00, NA, 1.89700E+00, NA, -1.34700E+00, 7.63400E+00, -1.45900E+00, 4.06300E-01, 1.71000E-01, -1.18100E+00, -2.21400E+00, 3.25800E+00, -3.25800E+00, NA, 4.95300E-01, NA, -1.10900E+00, NA, -7.23900E-01, NA, -7.66000E-01, 5.16200E-01, -9.30500E-01, 5.42600E-01, -9.61000E-01, NA, -3.41400E-01, NA, -1.71400E+00), .Dim=c(33, 2)), logit_pi_B= structure(.Data= c(-2.46400E+00, -4.61500E+00, -1.33800E+00, -4.74000E+00, -2.69300E+00, -4.69500E+00, -2.92200E+00, -3.76200E+00, -4.86000E+00, -4.86000E+00, NA, NA, -3.70100E+00, NA, -2.91400E+00, NA, -3.41400E+00, -4.35000E+00, NA, NA, -3.81800E+00, -4.52200E+00, -2.01100E+00, -4.96300E+00, -2.24000E+00, -2.43900E+00, -3.49700E+00, NA, -3.10800E+00, -4.52700E+00, -4.46000E+00, -3.52600E+00, NA, NA, NA, NA, -1.43800E+00, -2.60100E+00, NA, -3.09100E+00, -1.34700E+00, -3.33500E+00, -1.43900E+00, -4.13700E+00, -2.46800E+00, -3.25900E+00, -3.76100E+00, NA, -2.83300E+00, -5.08100E+00, -2.00400E+00, -3.27200E+00, -2.63100E+00, -3.63000E+00, -2.17500E+00, -4.98400E+00, -2.76000E+00, -5.01200E+00, -3.58700E+00, -4.58500E+00, -3.48800E+00, -3.67600E+00, -2.50500E+00, -3.65600E+00, -2.82500E+00, -5.27600E+00), .Dim=c(33, 2)), n= structure(.Data= c(6.40000E+01, 3.90000E+01, 9.30000E+01, 2.30000E+01, 1.91000E+02, 3.10000E+01, 1.41000E+02, 3.60000E+01, 1.06000E+02, 2.40000E+01, NA, NA, 7.50000E+01, 5.00000E+01, 1.68000E+02, 2.70000E+01, 1.23000E+02, 3.50000E+01, NA, NA, 7.80000E+01, 1.50000E+01, 1.38000E+02, 6.00000E+00, 3.30000E+01, 3.00000E+01, 2.40000E+01, 1.00000E+01, 2.66000E+02, 1.50000E+01, 8.40000E+01, 9.10000E+01, NA, NA, NA, NA, 1.06000E+02, 2.50000E+01, 1.30000E+01, 1.00000E+01, 7.30000E+01, 1.50000E+01, 2.68000E+02, 5.10000E+01, 1.28000E+02, 1.16000E+02, 1.86000E+02, 7.80000E+01, 1.56000E+02, 6.00000E+00, 7.50000E+01, 3.60000E+01, 1.58000E+02, 3.60000E+01, 1.23000E+02, 2.40000E+01, 1.22000E+02, 3.00000E+01, 2.05000E+02, 9.30000E+01, 1.41000E+02, 6.20000E+01, 1.22000E+02, 3.80000E+01, 1.74000E+02, 2.30000E+01), .Dim=c(33, 2)))
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modelCheck('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/model_adult-Bresle.R.txt')
modelData('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/data.txt')
modelCompile(1)
modelSetRN(1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs/inits1.txt',1)
modelGenInits()
modelUpdate(5000,2,5000)
samplesSet(pi_Eu00)
samplesSet(pi_Eu01)
samplesSet(logit_int_Eu)
samplesSet(logit_flow_Eu)
samplesSet(lflow_fall_Eu)
samplesSet(sigmapi_Eu)
samplesSet(epsilon_Eu)
samplesSet(mupi_B)
samplesSet(sigmapi_B)
samplesSet(pi_Eu)
samplesSet(p_Eu00_tot)
samplesSet(p_Eu01_tot)
samplesSet(pi_B)
samplesSet(test)
samplesSet(R2)
samplesSet(n_tot)
samplesSet(n_1SW)
samplesSet(n_MSW)
samplesSet(shape_lambda)
samplesSet(rate_lambda)
samplesSet(lambda_tot0)
samplesSet(Plambda0)
samplesSet(s)
samplesSet(lambda_tot)
samplesSet(Plambda)
summarySet(pi_Eu00)
summarySet(pi_Eu01)
summarySet(logit_int_Eu)
summarySet(logit_flow_Eu)
summarySet(lflow_fall_Eu)
summarySet(sigmapi_Eu)
summarySet(epsilon_Eu)
summarySet(mupi_B)
summarySet(sigmapi_B)
summarySet(pi_Eu)
summarySet(p_Eu00_tot)
summarySet(p_Eu01_tot)
summarySet(pi_B)
summarySet(test)
summarySet(R2)
summarySet(n_tot)
summarySet(n_1SW)
summarySet(n_MSW)
summarySet(shape_lambda)
summarySet(rate_lambda)
summarySet(lambda_tot0)
summarySet(Plambda0)
summarySet(s)
summarySet(lambda_tot)
summarySet(Plambda)
modelUpdate(25000,2,25000)
samplesCoda('*', '/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/adult/bugs//')
summaryStats('*')
modelQuit('y')
list(lambda_tot0=1.042E+02, logit_flow_Eu=c(-4.262E-01, -7.079E-01), lflow_fall_Eu=c(-5.000E-01, -5.000E-01), logit_int_Eu=c(5.082E-01, 8.304E-01), mupi_B=c(8.270E-02, 1.032E-01), pi_Eu00=c(5.871E-01, 5.992E-01), pi_Eu01=c(2.006E-01, 7.994E-01), rate_lambda=4.238E-02, s=c(1.567E+01, 3.914E+00), shape_lambda=5.995E+00, sigmapi_B=c(6.440E-01, 6.969E-01), sigmapi_Eu=c(1.019E+00, 8.865E-01), lambda_tot=c(1.020E+02, 1.155E+02, 2.208E+02, 1.762E+02, 1.300E+02, NA, 1.245E+02, 1.943E+02, 1.570E+02, NA, 9.300E+01, 1.440E+02, 6.233E+01, 3.400E+01, 2.805E+02, 1.750E+02, NA, NA, 1.303E+02, 2.300E+01, 8.723E+01, 3.182E+02, 2.432E+02, 2.640E+02, 1.620E+02, 1.094E+02, 1.935E+02, 1.470E+02, 1.512E+02, 2.970E+02, 2.024E+02, 1.589E+02, 1.965E+02), logit_pi_Eu= structure(.Data= c(1.682E+00, 3.922E-02, NA, -7.124E-01, NA, -1.571E+00, NA, -9.260E-01, 1.485E+00, -1.485E+00, NA, NA, 1.406E+00, 1.206E-01, NA, -1.414E+00, NA, -2.174E-01, NA, NA, 2.377E-01, -2.116E+00, -7.563E-01, -4.263E+00, 3.127E-01, 5.349E-02, NA, 3.567E-01, NA, -2.567E+00, -1.839E+00, -1.746E+00, NA, NA, NA, NA, 1.810E+00, -1.389E+00, NA, 1.897E+00, NA, -1.347E+00, 7.634E+00, -1.459E+00, 4.063E-01, 1.710E-01, -1.181E+00, -2.214E+00, 3.258E+00, -3.258E+00, NA, 4.953E-01, NA, -1.109E+00, NA, -7.239E-01, NA, -7.660E-01, 5.162E-01, -9.305E-01, 5.426E-01, -9.610E-01, NA, -3.414E-01, NA, -1.714E+00), .Dim=c(33, 2)), logit_pi_B= structure(.Data= c(-2.464E+00, -4.615E+00, -1.338E+00, -4.740E+00, -2.693E+00, -4.695E+00, -2.922E+00, -3.762E+00, -4.860E+00, -4.860E+00, NA, NA, -3.701E+00, NA, -2.914E+00, NA, -3.414E+00, -4.350E+00, NA, NA, -3.818E+00, -4.522E+00, -2.011E+00, -4.963E+00, -2.240E+00, -2.439E+00, -3.497E+00, NA, -3.108E+00, -4.527E+00, -4.460E+00, -3.526E+00, NA, NA, NA, NA, -1.438E+00, -2.601E+00, NA, -3.091E+00, -1.347E+00, -3.335E+00, -1.439E+00, -4.137E+00, -2.468E+00, -3.259E+00, -3.761E+00, NA, -2.833E+00, -5.081E+00, -2.004E+00, -3.272E+00, -2.631E+00, -3.630E+00, -2.175E+00, -4.984E+00, -2.760E+00, -5.012E+00, -3.587E+00, -4.585E+00, -3.488E+00, -3.676E+00, -2.505E+00, -3.656E+00, -2.825E+00, -5.276E+00), .Dim=c(33, 2)), n= structure(.Data= c(6.400E+01, 3.900E+01, 9.300E+01, 2.300E+01, 1.910E+02, 3.100E+01, 1.410E+02, 3.600E+01, 1.060E+02, 2.400E+01, NA, NA, 7.500E+01, 5.000E+01, 1.680E+02, 2.700E+01, 1.230E+02, 3.500E+01, NA, NA, 7.800E+01, 1.500E+01, 1.380E+02, 6.000E+00, 3.300E+01, 3.000E+01, 2.400E+01, 1.000E+01, 2.660E+02, 1.500E+01, 8.400E+01, 9.100E+01, NA, NA, NA, NA, 1.060E+02, 2.500E+01, 1.300E+01, 1.000E+01, 7.300E+01, 1.500E+01, 2.680E+02, 5.100E+01, 1.280E+02, 1.160E+02, 1.860E+02, 7.800E+01, 1.560E+02, 6.000E+00, 7.500E+01, 3.600E+01, 1.580E+02, 3.600E+01, 1.230E+02, 2.400E+01, 1.220E+02, 3.000E+01, 2.050E+02, 9.300E+01, 1.410E+02, 6.200E+01, 1.220E+02, 3.800E+01, 1.740E+02, 2.300E+01), .Dim=c(33, 2)))
......@@ -143,5 +143,7 @@ inits_updated <- list(
inits <- list(c( inits_fix,inits_updated))
save(inits,file=paste(paste('inits/inits_',stade,'.Rdata',sep="")))
#save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
bugs.inits(inits, n.chains=1,digits=3, inits.files = paste('inits/init-',site,'-',stade,year,'.txt',sep=""))
......@@ -12,15 +12,15 @@ heidel.diag also implements a convergence diagnostic, and removes up to half the
Stationarity start p-value
test iteration
shape_lambda passed 1 0.0754
rate_lambda passed 1 0.0988
lambda_tot0 passed 1 0.9441
Halfwidth Mean Halfwidth
test
shape_lambda passed 3.6837 0.128060
rate_lambda passed 0.0243 0.000869
lambda_tot0 passed 164.1569 4.714110
shape_lambda passed 10001 0.223
rate_lambda passed 2501 0.157
lambda_tot0 passed 1 0.593
Halfwidth Mean Halfwidth
test
shape_lambda passed 3.627 0.06632
rate_lambda passed 0.024 0.00042
lambda_tot0 passed 165.669 1.74561
---------------------------
Geweke's convergence diagnostic
......@@ -36,7 +36,7 @@ Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
shape_lambda rate_lambda lambda_tot0
0.977 0.813 0.024
2.851 3.231 -0.877
---------------------------
......@@ -48,7 +48,7 @@ Probability (s) = 0.95
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
shape_lambda 16 17506 3746 4.67
rate_lambda 16 18270 3746 4.88
lambda_tot0 8 9123 3746 2.44
shape_lambda 24 30996 3746 8.27
rate_lambda 24 30240 3746 8.07
lambda_tot0 12 17600 3746 4.70
This diff is collapsed.
......@@ -32,22 +32,31 @@ source(paste('parameters_',stade,'.R',sep="")) # chargement des paramètres
#------------------------INITS----------------------------------##
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
# if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
# if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(!file.exists(paste('inits/inits_',stade,year,'.Rdata',sep=""))){
if(!file.exists(paste("inits/init-",site,"-",stade,year,".txt",sep=""))){
source(paste('inits/inits_',stade,'.R',sep="")) # création des inits des données
#load(paste('inits/inits_',stade,year,'.Rdata',sep=""))
}
#load(paste('inits/inits_',stade,'.Rdata',sep="")) # chargement des inits
#if(site == "Bresle" && stade == "adult") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
#if(site == "Nivelle") {inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))}
inits <- list(read.bugsdata(paste("inits/init-",site,"-",stade,year,".txt",sep="")))
#------------------------MODEL----------------------------------##
model <- paste("model/",stade,"-",site,".R",sep="") # path of the model
if(site == "Scorff" && stade == "smolt") {model <- paste("model/",stade,"-",site,"_",year,".R",sep="")} # le modèle Scorrf pour les smolt peut changer tous les ans suivant conditions
model <- paste("model/model_",stade,"-",site,".R",sep="") # path of the model
if(site == "Scorff" && stade == "smolt") {model <- paste("model/model_",stade,"-",site,"_",year,".R",sep="")} # le modèle Scorrf pour les smolt peut changer tous les ans suivant conditions
model
filename <- file.path(work.dir, model)
#system(paste("cp",model,paste(stade,"-",site,".txt",sep=""),sep=""))
#---------------------------ANALYSIS-----------------------------##
nChains = length(inits) # Number of chains to run.
adaptSteps = 1000 # Number of steps to "tune" the samplers.
nburnin=1000 # Number of steps to "burn-in" the samplers.
nstore=5000 # Total number of steps in chains to save.
nthin=1 # Number of steps to "thin" (1=keep every step).
nburnin=5000 # Number of steps to "burn-in" the samplers.
nstore=25000 # Total number of steps in chains to save.
nthin=2 # Number of steps to "thin" (1=keep every step).
#nPerChain = ceiling( ( numSavedSteps * thinSteps ) / nChains ) # Steps per chain.
### Start of the run ###
......@@ -57,15 +66,25 @@ start.time = Sys.time(); cat("Start of the run\n");
fit <- bugs(
data
,inits
,model.file = model
,model.file = filename
,parameters
,n.chains = nChains, n.iter = nstore + nburnin, n.burnin = nburnin, n.thin = nthin
,DIC=FALSE
,codaPkg = FALSE, clearWD=TRUE
,codaPkg = FALSE, clearWD=FALSE
#,debug=TRUE
,working.directory=work.dir
,working.directory=paste(work.dir,"bugs",sep="/")
)
## cleaning
system("rm bugs/CODA*")
### Save inits ###
# save last values for inits
# inits <- fit$last.values
# if(site == "Nivelle") {
# save(inits,file=paste('inits/inits_',stade,year,'.Rdata',sep=""))
# }
######### JAGS ##########
## Compile & adapt
......@@ -159,6 +178,6 @@ dev.off()
#------------------------------------------------------------------------------
## SUMMARY
if(site == "Scorff" && stade == "adult") {source("summary_adult.R")}
#if(site == "Scorff" && stade == "adult") {source("summary_adult.R")}
if(site == "Nivelle" && stade == "tacon") {source("analyse_coda_tacon.R")}
list(Nyears=3.50000E+01, NBeau=3.00000E+01, NEu=2.20000E+01, C_B=c(1.11500E+03, 1.12800E+03, 7.50000E+02, 1.53000E+03, 7.47000E+02, 4.00000E+02, NA, NA, NA, NA, 4.15000E+02, 5.65000E+02, 9.41000E+02, 5.00000E+01, 4.20000E+01, 2.59400E+03, 8.00000E+02, 4.00000E+01, 2.63000E+02, NA, 6.30000E+01, 7.74000E+02, 4.32100E+03, 2.11000E+03, 1.07400E+03, 2.21500E+03, 2.15500E+03, 3.17000E+02, 1.13500E+03, 1.82900E+03, 1.93800E+03, 5.23000E+02, 4.24000E+02, 1.96800E+03, 3.48200E+03), D_B=c(0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, NA, NA, NA, NA, 0.00000E+00, 1.50000E+01, 2.00000E+01, 0.00000E+00, 2.00000E+00, 6.00000E+00, 3.00000E+01, 0.00000E+00, 9.00000E+00, NA, 0.00000E+00, 1.00000E+01, 5.40000E+01, 6.00000E+01, 3.20000E+01, 3.00000E+01, 6.00000E+01, 7.00000E+00, 1.00000E+01, 2.90000E+01, 2.80000E+01, 1.00000E+01, 4.00000E+00, 6.00000E+00, 0.00000E+00), Cm_B=c( NA, NA, NA, NA, 7.45000E+02, NA, NA, NA, NA, NA, NA, 5.50000E+02, 9.20000E+02, NA, 4.00000E+01, 2.58800E+03, 7.70000E+02, 4.00000E+01, 2.53000E+02, NA, NA, 7.64000E+02, 4.26700E+03, 2.05000E+03, 1.04000E+03, 2.17500E+03, 2.09000E+03, 3.10000E+02, 1.12000E+03, 1.80000E+03, 1.91000E+03, 5.13000E+02, 4.20000E+02, 1.93200E+03, 3.40900E+03), Cum_B=c( NA, NA, NA, NA, 2.00000E+00, NA, NA, NA, NA, NA, NA, 0.00000E+00, 1.00000E+00, NA, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, NA, NA, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 1.00000E+01, 5.00000E+00, 0.00000E+00, 5.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 3.00000E+01, 7.30000E+01), Cm_Eu=c( NA, NA, NA, NA, 6.10000E+01, NA, NA, NA, NA, NA, NA, 6.10000E+01, 7.70000E+01, NA, 2.00000E+00, 2.30000E+02, 7.80000E+01, 3.00000E+00, 2.00000E+01, NA, NA, 6.60000E+01, 2.87000E+02, 1.58000E+02, 7.00000E+01, 1.23000E+02, 1.94000E+02, 3.20000E+01, 4.10000E+01, 1.86000E+02, 1.84000E+02, 2.60000E+01, 2.10000E+01, 1.05000E+02, 2.58000E+02), Cum_Eu=c( NA, NA, NA, NA, 9.70000E+01, NA, NA, NA, NA, NA, NA, 1.22000E+02, 1.21000E+02, NA, 1.53000E+02, 3.27000E+02, 8.40000E+01, 4.70000E+01, 1.09000E+02, NA, NA, 1.86000E+02, 2.22000E+02, 2.44000E+02, 1.13000E+02, 1.90000E+02, 4.17000E+02, 7.70000E+01, 7.20000E+01, 4.76000E+02, 4.30000E+02, 7.40000E+01, 1.30000E+02, 3.20000E+02, 3.30000E+02), Q_Eu=c(7.76845E+03, 6.64299E+03, 1.14102E+04, 4.40484E+03, 4.64964E+03, 5.34259E+03, 9.20198E+03, 1.00125E+04, 8.34250E+03, 5.74725E+03, 5.51175E+03, 5.56950E+03, 6.39950E+03, 1.00988E+04, 7.41075E+03, 7.94975E+03, 5.68200E+03, 7.67750E+03, 9.03925E+03, 9.64125E+03, 8.48000E+03, 8.31375E+03))
list(mu_B=5.00000E-01, sigmap_B=1.00000E+00, logit_int_Eu=1.00000E+00, logit_flow_Eu=1.00000E+00, sigmap_Eu=1.00000E+00, shape_lambda=2.50000E+00, rate_lambda=1.00000E-02, Ntot=c( NA, NA, NA, NA, 1.93100E+03, NA, NA, NA, NA, NA, NA, 1.65000E+03, 2.36600E+03, NA, 3.10000E+03, 6.26700E+03, 1.59900E+03, 6.66000E+02, 1.63200E+03, NA, NA, 2.91700E+03, 7.56700E+03, 5.21500E+03, 2.72000E+03, 5.54400E+03, 6.58700E+03, 1.05500E+03, 3.09100E+03, 6.40600E+03, 6.37300E+03, 1.97300E+03, 3.02000E+03, 7.85000E+03, 7.84200E+03), lambda=c( NA, NA, NA, NA, 1.92100E+03, NA, NA, NA, NA, NA, NA, 1.64000E+03, 2.35600E+03, NA, 3.09000E+03, 6.25700E+03, 1.58900E+03, 6.56000E+02, 1.62200E+03, NA, NA, 2.90700E+03, 7.55700E+03, 5.20500E+03, 2.71000E+03, 5.53400E+03, 6.57700E+03, 1.04500E+03, 3.08100E+03, 6.39600E+03, 6.36300E+03, 1.96300E+03, 3.01000E+03, 7.84000E+03, 7.83200E+03), logit_pi_B=c(5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01), logit_pi_Eu=c(5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01, 5.00000E-01))
OpenBUGS version 3.2.3 rev 1012
model is syntactically correct
data loaded
model compiled
initial values loaded but chain contain uninitialized variables
initial values generated, model initialized
5000 updates took 11 s
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25000 updates took 73 s
CODA files written
Summary statistics
mean sd val2.5pc median val97.5pc sample
Nesc[1] 4328.0 1258.0 2981.0 3670.0 7305.0 25000
Nesc[2] 4095.0 1087.0 2756.0 3128.0 6658.0 25000
Nesc[3] 2332.0 932.7 1464.0 2683.0 4759.0 25000
Nesc[4] 5314.0 956.2 3590.0 5433.0 6685.0 25000
Nesc[5] 2017.0 189.3 1701.0 2004.0 2428.0 25000
Nesc[6] 2554.0 1152.0 689.7 1824.0 2746.0 25000
Nesc[11] 2686.0 1083.0 953.5 2420.0 3038.0 25000
Nesc[12] 1757.0 170.9 1479.0 1729.0 2112.0 25000
Nesc[13] 2456.0 210.8 2078.0 2432.0 2896.0 25000
Nesc[14] 2552.0 1896.0 381.6 1891.0 7519.0 25000
Nesc[15] 1702.0 421.3 1030.0 1566.0 2519.0 25000
Nesc[16] 6274.0 303.5 5720.0 6191.0 6913.0 25000
Nesc[17] 1673.0 132.6 1454.0 1664.0 1961.0 25000
Nesc[18] 679.4 200.2 395.9 624.0 1180.0 25000
Nesc[19] 1794.0 276.1 1293.0 1757.0 2371.0 25000
Nesc[21] 2735.0 1729.0 451.6 3485.0 5983.0 25000
Nesc[22] 2987.0 291.7 2475.0 3039.0 3607.0 25000
Nesc[23] 7579.0 273.7 7092.0 7553.0 8163.0 25000
Nesc[24] 5182.0 294.2 4670.0 5104.0 5719.0 25000
Nesc[25] 2724.0 236.5 2312.0 2681.0 3293.0 25000
Nesc[26] 5428.0 341.1 4867.0 5505.0 6172.0 25000
Nesc[27] 6634.0 360.2 5964.0 6614.0 7396.0 25000
Nesc[28] 1180.0 161.0 936.8 1146.0 1535.0 25000
Nesc[29] 2895.0 295.7 2389.0 2866.0 3318.0 25000
Nesc[30] 6469.0 375.2 5778.0 6467.0 7248.0 25000
Nesc[31] 6489.0 371.9 5795.0 6713.0 7254.0 25000
Nesc[32] 1883.0 264.2 1458.0 1826.0 2465.0 25000
Nesc[33] 2763.0 428.8 2068.0 2711.0 3737.0 25000
Nesc[34] 7518.0 607.6 6500.0 7566.0 8872.0 25000
Nesc[35] 7793.0 352.7 7160.0 7796.0 8558.0 25000
Ntot[1] 4328.0 1258.0 2981.0 3670.0 7305.0 25000
Ntot[2] 4095.0 1087.0 2756.0 3128.0 6658.0 25000
Ntot[3] 2332.0 932.7 1464.0 2683.0 4759.0 25000
Ntot[4] 5314.0 956.2 3590.0 5433.0 6685.0 25000
Ntot[5] 2017.0 189.3 1701.0 2004.0 2428.0 25000
Ntot[6] 2554.0 1152.0 689.7 1824.0 2746.0 25000
Ntot[11] 2686.0 1083.0 953.5 2420.0 3038.0 25000
Ntot[12] 1772.0 170.9 1494.0 1744.0 2127.0 25000
Ntot[13] 2476.0 210.8 2098.0 2452.0 2916.0 25000
Ntot[14] 2552.0 1896.0 381.6 1891.0 7519.0 25000
Ntot[15] 1704.0 421.3 1032.0 1568.0 2521.0 25000
Ntot[16] 6280.0 303.5 5726.0 6197.0 6919.0 25000
Ntot[17] 1703.0 132.6 1484.0 1694.0 1991.0 25000
Ntot[18] 679.4 200.2 395.9 624.0 1180.0 25000
Ntot[19] 1803.0 276.1 1302.0 1766.0 2380.0 25000
Ntot[21] 2735.0 1729.0 451.6 3485.0 5983.0 25000
Ntot[22] 2997.0 291.7 2485.0 3049.0 3617.0 25000
Ntot[23] 7633.0 273.7 7146.0 7607.0 8217.0 25000
Ntot[24] 5242.0 294.2 4730.0 5164.0 5779.0 25000
Ntot[25] 2756.0 236.5 2344.0 2713.0 3325.0 25000
Ntot[26] 5458.0 341.1 4897.0 5535.0 6202.0 25000
Ntot[27] 6694.0 360.2 6024.0 6674.0 7456.0 25000
Ntot[28] 1187.0 161.0 943.8 1153.0 1542.0 25000
Ntot[29] 2905.0 295.7 2399.0 2876.0 3328.0 25000
Ntot[30] 6498.0 375.2 5807.0 6496.0 7277.0 25000
Ntot[31] 6517.0 371.9 5823.0 6741.0 7282.0 25000
Ntot[32] 1893.0 264.2 1468.0 1836.0 2475.0 25000
Ntot[33] 2767.0 428.8 2072.0 2715.0 3741.0 25000
Ntot[34] 7524.0 607.6 6506.0 7572.0 8878.0 25000
Ntot[35] 7793.0 352.7 7160.0 7796.0 8558.0 25000
R2 0.03745 0.1094 -0.2112 0.04271 0.2169 25000
epsilon_B[1] -0.04685 0.4672 -0.6192 -0.06601 0.8316 25000
epsilon_B[2] 0.04314 0.4859 -0.9109 0.05113 0.7528 25000
epsilon_B[3] 0.4119 0.6817 -0.5116 0.4168 1.588 25000
epsilon_B[4] 0.08145 0.3405 -0.4925 0.05545 0.6992 25000
epsilon_B[5] 0.4911 0.2743 -0.03919 0.4874 1.012 25000
epsilon_B[6] -0.6619 0.729 -1.469 -0.8402 0.8634 25000
epsilon_B[7] -0.7148 0.6693 -1.538 -0.8024 0.6832 25000
epsilon_B[8] 0.2243 0.265 -0.2933 0.2269 0.7222 25000
epsilon_B[9] 0.5405 0.2679 0.04699 0.5336 1.039 25000
epsilon_B[10] -0.6543 1.032 -2.447 -0.6644 1.411 25000
epsilon_B[11] -3.081 0.5454 -4.204 -3.064 -2.072 25000
epsilon_B[12] 0.6971 0.2436 0.2362 0.6983 1.161 25000
epsilon_B[13] 0.975 0.3027 0.4037 0.9698 1.573 25000
epsilon_B[14] -2.03 0.4874 -3.055 -1.995 -1.163 25000
epsilon_B[15] -0.1064 0.605 -0.8984 -0.2039 1.232 25000
epsilon_B[16] -0.4729 1.008 -2.262 -0.5029 1.587 25000
epsilon_B[17] -0.1242 0.2641 -0.6485 -0.1237 0.3641 25000
epsilon_B[18] 1.424 0.3081 0.8455 1.419 2.033 25000
epsilon_B[19] 0.6486 0.2502 0.1745 0.6443 1.144 25000
epsilon_B[20] 0.5899 0.2703 0.08891 0.5895 1.104 25000
epsilon_B[21] 0.665 0.2535 0.1807 0.6634 1.158 25000
epsilon_B[22] 0.2337 0.2262 -0.1912 0.2347 0.652 25000
epsilon_B[23] -0.06166 0.2931 -0.6388 -0.05716 0.4984 25000
epsilon_B[24] 0.596 0.2909 0.03171 0.5926 1.155 25000
epsilon_B[25] 0.006608 0.2321 -0.4353 0.007387 0.4505 25000
epsilon_B[26] 0.0968 0.2252 -0.331 0.09721 0.5254 25000
epsilon_B[27] -0.007469 0.2964 -0.5899 -0.003578 0.5467 25000
epsilon_B[28] -0.8825 0.3304 -1.555 -0.8683 -0.3149 25000
epsilon_B[29] -0.108 0.2384 -0.5744 -0.1034 0.3301 25000
epsilon_B[30] 0.8596 0.2537 0.3819 0.8554 1.348 25000
epsilon_Eu[1] 0.1808 0.477 -0.7584 0.1826 1.113 25000
epsilon_Eu[2] 1.045 0.5103 0.09005 1.033 2.071 25000
epsilon_Eu[3] 0.6608 0.5724 -0.4497 0.6511 1.805 25000
epsilon_Eu[4] 0.2632 0.8562 -1.326 0.2507 1.949 25000
epsilon_Eu[5] 0.1392 0.5195 -0.85 0.1345 1.148 25000
epsilon_Eu[6] 0.5629 0.521 -0.4457 0.5572 1.577 25000
epsilon_Eu[7] 0.208 0.8885 -1.613 0.2356 1.774 25000
epsilon_Eu[8] 0.1519 0.6471 -1.073 0.1482 1.429 25000
epsilon_Eu[9] 0.5304 0.4815 -0.3923 0.5208 1.49 25000
epsilon_Eu[10] -0.6858 0.3923 -1.455 -0.6836 0.07081 25000
epsilon_Eu[11] -0.1895 0.4326 -1.032 -0.1882 0.6391 25000
epsilon_Eu[12] -0.6734 0.4968 -1.656 -0.6614 0.2638 25000
epsilon_Eu[13] -1.104 0.4236 -1.984 -1.087 -0.314 25000
epsilon_Eu[14] 1.063 0.4809 0.1479 1.054 2.026 25000
epsilon_Eu[15] 0.6939 0.5929 -0.4868 0.6922 1.857 25000
epsilon_Eu[16] -2.101 0.5509 -3.237 -2.078 -1.089 25000
epsilon_Eu[17] 0.8843 0.451 0.04593 0.8715 1.796 25000
epsilon_Eu[18] 0.8885 0.3902 0.1764 0.8739 1.691 25000
epsilon_Eu[19] -0.9385 0.603 -2.147 -0.9228 0.2095 25000
epsilon_Eu[20] -0.8094 0.6233 -2.072 -0.8009 0.3728 25000
epsilon_Eu[21] -0.8976 0.4424 -1.806 -0.8734 -0.08474 25000
epsilon_Eu[22] 0.1201 0.3501 -0.5695 0.1188 0.8065 25000
lambda[1] 4328.0 1258.0 2958.0 5443.0 7310.0 25000
lambda[2] 4095.0 1087.0 2747.0 3864.0 6660.0 25000
lambda[3] 2332.0 932.4 1457.0 3650.0 4755.0 25000
lambda[4] 5313.0 957.9 3592.0 5332.0 7331.0 25000
lambda[5] 2018.0 194.5 1678.0 2005.0 2440.0 25000
lambda[6] 2556.0 1152.0 1156.0 3054.0 5064.0 25000
lambda[11] 2687.0 1083.0 951.2 2425.0 4903.0 25000
lambda[12] 1773.0 175.2 1478.0 1760.0 2157.0 25000
lambda[13] 2477.0 216.6 2119.0 2464.0 2928.0 25000
lambda[14] 2553.0 1895.0 373.8 1891.0 7518.0 25000
lambda[15] 1706.0 423.3 937.1 1721.0 2636.0 25000
lambda[16] 6279.0 313.2 5705.0 6279.0 6933.0 25000
lambda[17] 1704.0 138.6 1449.0 1704.0 2001.0 25000
lambda[18] 681.4 201.9 411.2 629.4 1182.0 25000
lambda[19] 1804.0 278.7 1307.0 1841.0 2389.0 25000
lambda[21] 2736.0 1728.0 454.8 1675.0 6667.0 25000
lambda[22] 2997.0 297.1 2472.0 2966.0 3629.0 25000
lambda[23] 7631.0 287.4 7109.0 7629.0 8236.0 25000
lambda[24] 5241.0 302.3 4707.0 5293.0 5861.0 25000
lambda[25] 2757.0 241.6 2335.0 2710.0 3340.0 25000
lambda[26] 5456.0 348.5 4843.0 5538.0 6207.0 25000
lambda[27] 6692.0 368.8 6005.0 6669.0 7475.0 25000
lambda[28] 1189.0 164.9 917.0 1180.0 1565.0 25000
lambda[29] 2905.0 300.1 2392.0 2784.0 3558.0 25000
lambda[30] 6497.0 383.2 5790.0 6468.0 7286.0 25000
lambda[31] 6515.0 380.2 5807.0 6401.0 7298.0 25000
lambda[32] 1894.0 267.1 1462.0 1828.0 2476.0 25000
lambda[33] 2768.0 431.9 2062.0 2759.0 3745.0 25000
lambda[34] 7521.0 614.2 6495.0 7671.0 8889.0 25000
lambda[35] 7790.0 363.3 7122.0 7734.0 8568.0 25000
logit_flow_Eu -0.08044 0.0766 -0.2348 -0.07935 0.06661 25000
logit_int_Eu -2.501 0.07223 -2.644 -2.5 -2.357 25000
mean_gamma 3816.0 477.6 3016.0 3941.0 4838.0 25000
mu_B 0.2818 0.03633 0.2173 0.2804 0.3533 25000
p_B[1] 0.2786 0.07839 0.1965 0.256 0.4445 25000
p_B[2] 0.2945 0.0764 0.171 0.2865 0.4057 25000
p_B[3] 0.3682 0.1281 0.217 0.3354 0.6092 25000
p_B[4] 0.2971 0.05388 0.2212 0.2893 0.3922 25000
p_B[5] 0.3729 0.03562 0.3028 0.3735 0.443 25000
p_B[6] 0.1988 0.1105 0.1027 0.1437 0.461 25000
p_B[7] 0.1884 0.1001 0.09745 0.1637 0.4578 25000
p_B[8] 0.3218 0.03186 0.2606 0.3221 0.384 25000
p_B[9] 0.3825 0.03386 0.3197 0.3817 0.4514 25000
p_B[10] 0.2142 0.1463 0.03402 0.1774 0.5876 25000
p_B[11] 0.02861 0.008598 0.01373 0.02673 0.05 25000
p_B[12] 0.4139 0.02091 0.3728 0.4144 0.4541 25000
p_B[13] 0.4718 0.0381 0.4004 0.4716 0.5448 25000
p_B[14] 0.06737 0.02032 0.03316 0.06554 0.1141 25000
p_B[15] 0.2721 0.1055 0.1572 0.2414 0.558 25000
p_B[16] 0.2375 0.1534 0.04761 0.2028 0.6249 25000
p_B[17] 0.2609 0.0264 0.2111 0.2598 0.314 25000
p_B[18] 0.5665 0.02091 0.5254 0.5661 0.6071 25000
p_B[19] 0.4038 0.02328 0.3586 0.4038 0.4473 25000
p_B[20] 0.3923 0.0341 0.3268 0.3924 0.4574 25000
p_B[21] 0.4074 0.02612 0.3558 0.4064 0.4572 25000
p_B[22] 0.3229 0.01817 0.2862 0.3227 0.3595 25000
p_B[23] 0.2718 0.03744 0.2022 0.2703 0.3456 25000
p_B[24] 0.3941 0.03922 0.3228 0.3939 0.4742 25000
p_B[25] 0.2824 0.01727 0.2487 0.2814 0.3185 25000
p_B[26] 0.2982 0.01794 0.2637 0.2974 0.3353 25000
p_B[27] 0.2813 0.03887 0.209 0.2796 0.3604 25000
p_B[28] 0.1573 0.0248 0.1134 0.1554 0.2062 25000
p_B[29] 0.2633 0.02145 0.2229 0.2633 0.3036 25000
p_B[30] 0.4475 0.02084 0.4047 0.4469 0.488 25000
p_B00 0.6124 0.1972 0.267 0.6005 0.8822 25000
p_B02 0.2298 0.2133 0.0267 0.1464 0.8489 25000
p_B95 0.2448 0.2189 0.02253 0.17 0.6226 25000
p_Btot[1] 0.2786 0.07839 0.1965 0.256 0.4445 25000
p_Btot[2] 0.2945 0.0764 0.171 0.2865 0.4057 25000
p_Btot[3] 0.3682 0.1281 0.217 0.3354 0.6092 25000
p_Btot[4] 0.2971 0.05388 0.2212 0.2893 0.3922 25000
p_Btot[5] 0.3729 0.03562 0.3028 0.3735 0.443 25000
p_Btot[6] 0.1988 0.1105 0.1027 0.1437 0.461 25000
p_Btot[7] 0.1884 0.1001 0.09745 0.1637 0.4578 25000
p_Btot[8] 0.3218 0.03186 0.2606 0.3221 0.384 25000
p_Btot[9] 0.3825 0.03386 0.3197 0.3817 0.4514 25000
p_Btot[10] 0.03825 0.04369 0.006013 0.02541 0.09841 25000
p_Btot[11] 0.02861 0.008598 0.01373 0.02673 0.05 25000
p_Btot[12] 0.4139 0.02091 0.3728 0.4144 0.4541 25000
p_Btot[13] 0.4718 0.0381 0.4004 0.4716 0.5448 25000
p_Btot[14] 0.06737 0.02032 0.03316 0.06554 0.1141 25000
p_Btot[15] 0.1493 0.02432 0.1071 0.1472 0.2038 25000
p_Btot[16] 0.04192 0.04698 0.007505 0.02583 0.1056 25000
p_Btot[17] 0.2609 0.0264 0.2111 0.2598 0.314 25000
p_Btot[18] 0.5665 0.02091 0.5254 0.5661 0.6071 25000
p_Btot[19] 0.4038 0.02328 0.3586 0.4038 0.4473 25000
p_Btot[20] 0.3923 0.0341 0.3268 0.3924 0.4574 25000
p_Btot[21] 0.4074 0.02612 0.3558 0.4064 0.4572 25000
p_Btot[22] 0.3229 0.01817 0.2862 0.3227 0.3595 25000
p_Btot[23] 0.2718 0.03744 0.2022 0.2703 0.3456 25000
p_Btot[24] 0.3941 0.03922 0.3228 0.3939 0.4742 25000
p_Btot[25] 0.2824 0.01727 0.2487 0.2814 0.3185 25000
p_Btot[26] 0.2982 0.01794 0.2637 0.2974 0.3353 25000
p_Btot[27] 0.2813 0.03887 0.209 0.2796 0.3604 25000
p_Btot[28] 0.1573 0.0248 0.1134 0.1554 0.2062 25000
p_Btot[29] 0.2633 0.02145 0.2229 0.2633 0.3036 25000
p_Btot[30] 0.4475 0.02084 0.4047 0.4469 0.488 25000
p_Eu[1] 0.07857 0.008759 0.0611 0.07824 0.09777 25000
p_Eu[2] 0.1028 0.01171 0.08006 0.1024 0.1281 25000
p_Eu[3] 0.08022 0.008475 0.06326 0.07974 0.09933 25000
p_Eu[4] 0.09614 0.02451 0.05824 0.09182 0.1543 25000
p_Eu[5] 0.08897 0.005491 0.07689 0.08894 0.1019 25000
p_Eu[6] 0.09602 0.009864 0.07615 0.09556 0.1169 25000
p_Eu[7] 0.07739 0.01934 0.04357 0.07601 0.1199 25000
p_Eu[8] 0.07323 0.01197 0.05133 0.07199 0.1009 25000
p_Eu[9] 0.08449 0.009223 0.06622 0.0842 0.1052 25000
p_Eu[10] 0.06756 0.003714 0.05713 0.06745 0.07772 25000
p_Eu[11] 0.07799 0.005672 0.06447 0.0778 0.091 25000
p_Eu[12] 0.06854 0.007229 0.05365 0.06844 0.08494 25000
p_Eu[13] 0.05866 0.004753 0.04709 0.05835 0.07017 25000
p_Eu[14] 0.09174 0.005966 0.07882 0.09171 0.1058 25000
p_Eu[15] 0.09165 0.01358 0.06634 0.09091 0.1205 25000
p_Eu[16] 0.04208 0.005419 0.02922 0.04198 0.0553 25000
p_Eu[17] 0.1022 0.00696 0.08768 0.1018 0.1175 25000
p_Eu[18] 0.09438 0.006414 0.08087 0.09412 0.1091 25000
p_Eu[19] 0.05589 0.008634 0.03849 0.05551 0.07516 25000
p_Eu[20] 0.05689 0.009184 0.03894 0.05655 0.07746 25000
p_Eu[21] 0.05739 0.005238 0.04486 0.05694 0.06968 25000
p_Eu[22] 0.07555 0.004473 0.06392 0.07526 0.08631 25000
rate_lambda 6.846E-4 1.975E-4 -0.006034 6.777E-4 0.007214 25000
shape_lambda 2.579 0.7017 1.409 2.505 4.125 25000
sigmap_B 0.8708 0.1468 0.629 0.8546 1.203 25000
sigmap_Eu 0.296 0.06271 0.1953 0.288 0.4396 25000
test 0.1417 0.3487 -0.006345 -4.469E-7 1.0 25000
var_gamma 6.178E+6 2.512E+6 3.198E+6 5.868E+6 1.219E+7 25000
modelCheck('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/smolt/bugs/model_smolt-Bresle.R.txt')
modelData('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/smolt/bugs/data.txt')
modelCompile(1)
modelSetRN(1)
modelInits('/home/basp-meco88/Documents/RESEARCH/PROJECTS/ORE/Abundance/Bresle/smolt/bugs/inits1.txt',1)
modelGenInits()
modelUpdate(5000,2,5000)
samplesSet(mu_B)
samplesSet(sigmap_B)
samplesSet(logit_int_Eu)
samplesSet(logit_flow_Eu)
samplesSet(sigmap_Eu)
samplesSet(p_B)
samplesSet(p_B95)
samplesSet(p_B00)
samplesSet(p_B02)
samplesSet(p_Btot)
samplesSet(epsilon_B)
samplesSet(p_Eu)
samplesSet(epsilon_Eu)
samplesSet(Ntot)
samplesSet(shape_lambda)